The Photopic Negative Response of the Mouse Electroretinogram: Reduction by Acute Elevation of Intraocular Pressure

2013 ◽  
Vol 54 (7) ◽  
pp. 4691 ◽  
Author(s):  
Vicki Chrysostomou ◽  
Jonathan G. Crowston
Keyword(s):  
Intraocular Pressure ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Acute Elevation

scholarly journals Short-Term Changes in the Photopic Negative Response Following Intraocular Pressure Lowering in Glaucoma

2020 ◽  
Vol 61 (10) ◽  
pp. 16
Author(s):  
Jessica Tang ◽  
Flora Hui ◽  
Xavier Hadoux ◽  
Bernardo Soares ◽  
Michael Jamieson ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Short Term ◽  
Pressure Lowering

Retrobulbar blood flow in rat eyes during acute elevation of intraocular pressure

2021 ◽  
Vol 207 ◽  
pp. 108606
Author(s):  
Ronald H. Silverman ◽  
Raksha Urs ◽  
Gulgun Tezel ◽  
Xiangjun Yang ◽  
Inez Nelson ◽  
...  
Keyword(s):  
Blood Flow ◽  
Intraocular Pressure ◽  
Acute Elevation ◽  
Retrobulbar Blood Flow

scholarly journals Comparison of Photopic Negative Response of Full-Field and Focal Electroretinograms in Detecting Glaucomatous Eyes

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Shigeki Machida ◽  
Kunifusa Tamada ◽  
Taku Oikawa ◽  
Yasutaka Gotoh ◽  
Tomoharu Nishimura ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Diagnostic Ability ◽  
Full Field ◽  
Receiver Operating Characteristic Curves ◽  
Advanced Glaucoma ◽  
Blue Background ◽  
Higher Sensitivity

Purpose. To compare the photopic negative response (PhNR) of the full-field electroretinogram (ERG) to the PhNR of the focal ERGs in detecting glaucoma.Methods. One hundred and three eyes with glaucoma and 42 normal eyes were studied. Full-field ERGs were elicited by red stimuli on a blue background. The focal ERGs were elicited by a15∘white stimulus spot centered on the macula, the superotemporal or the inferotemporal areas of the macula.Results. In early glaucoma, the areas under the receiver operating characteristic curves (AUCs) were significantly larger for the focal PhNR (0.863–0.924) than those for the full-field PhNR (0.666–0.748) (P<.05). The sensitivity was significantly higher for the focal PhNR than for the full-field PhNR in early (P<.01) and intermediate glaucoma (P<.05). In advanced glaucoma, there was no difference in the AUCs and sensitivities between the focal and full-field PhNRs.Conclusions. The focal ERG has the diagnostic ability with higher sensitivity in detecting early and intermediate glaucoma than the full-field ERG.

scholarly journals Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

2021 ◽  
Vol 8 ◽  
Author(s):  
Tina Diao ◽  
Fareshta Kushzad ◽  
Megh D. Patel ◽  
Megha P. Bindiganavale ◽  
Munam Wasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Optic Neuropathy ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Learning Approaches ◽  
Data Set ◽  
Full Field ◽  
Handheld Device ◽  
Technical Requirements

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p &lt; 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

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